Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations1481
Missing cells94
Missing cells (%)0.2%
Duplicate rows7
Duplicate rows (%)0.5%
Total size in memory1.4 MiB
Average record size in memory984.3 B

Variable types

Text1
Numeric15
Categorical19
Boolean3

Alerts

EmployeeCount has constant value "1.0"Constant
Over18 has constant value "True"Constant
StandardHours has constant value "80.0"Constant
Dataset has 7 (0.5%) duplicate rowsDuplicates
Age is highly overall correlated with AgeGroup and 1 other fieldsHigh correlation
AgeGroup is highly overall correlated with AgeHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
JobLevel is highly overall correlated with JobRole and 3 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 2 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 2 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
SalarySlab is highly overall correlated with JobLevel and 3 other fieldsHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly overall correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager has 58 (3.9%) missing valuesMissing
NumCompaniesWorked has 200 (13.5%) zerosZeros
TrainingTimesLastYear has 55 (3.7%) zerosZeros
YearsAtCompany has 44 (3.0%) zerosZeros
YearsInCurrentRole has 244 (16.5%) zerosZeros
YearsSinceLastPromotion has 586 (39.6%) zerosZeros
YearsWithCurrManager has 253 (17.1%) zerosZeros

Reproduction

Analysis started2024-08-26 16:04:13.943197
Analysis finished2024-08-26 16:04:43.840563
Duration29.9 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

EmpID
Text

Distinct1471
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size90.3 KiB
2024-08-26T21:34:44.093265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length5
Mean length5.3369345
Min length5

Characters and Unicode

Total characters7904
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1461 ?
Unique (%)98.6%

Sample

1st rowRM297
2nd rowRM302
3rd rowRM458
4th rowRM728
5th rowRM829
ValueCountFrequency (%)
rm1467 2
 
0.1%
rm1470 2
 
0.1%
rm1462 2
 
0.1%
rm1465 2
 
0.1%
rm1463 2
 
0.1%
rm1461 2
 
0.1%
pandas 2
 
0.1%
rm1466 2
 
0.1%
rm1468 2
 
0.1%
rm1469 2
 
0.1%
Other values (1463) 1464
98.7%
2024-08-26T21:34:44.506576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1480
18.7%
M 1480
18.7%
1 979
12.4%
2 498
 
6.3%
3 498
 
6.3%
0 496
 
6.3%
4 479
 
6.1%
6 407
 
5.1%
5 398
 
5.0%
7 390
 
4.9%
Other values (13) 799
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4921
62.3%
Uppercase Letter 2960
37.4%
Lowercase Letter 20
 
0.3%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 979
19.9%
2 498
10.1%
3 498
10.1%
0 496
10.1%
4 479
9.7%
6 407
8.3%
5 398
8.1%
7 390
 
7.9%
8 388
 
7.9%
9 388
 
7.9%
Lowercase Letter
ValueCountFrequency (%)
a 5
25.0%
p 3
15.0%
s 3
15.0%
n 2
 
10.0%
d 2
 
10.0%
i 1
 
5.0%
m 1
 
5.0%
o 1
 
5.0%
r 1
 
5.0%
t 1
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
R 1480
50.0%
M 1480
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4924
62.3%
Latin 2980
37.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1480
49.7%
M 1480
49.7%
a 5
 
0.2%
p 3
 
0.1%
s 3
 
0.1%
n 2
 
0.1%
d 2
 
0.1%
i 1
 
< 0.1%
m 1
 
< 0.1%
o 1
 
< 0.1%
Other values (2) 2
 
0.1%
Common
ValueCountFrequency (%)
1 979
19.9%
2 498
10.1%
3 498
10.1%
0 496
10.1%
4 479
9.7%
6 407
8.3%
5 398
8.1%
7 390
 
7.9%
8 388
 
7.9%
9 388
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1480
18.7%
M 1480
18.7%
1 979
12.4%
2 498
 
6.3%
3 498
 
6.3%
0 496
 
6.3%
4 479
 
6.1%
6 407
 
5.1%
5 398
 
5.0%
7 390
 
4.9%
Other values (13) 799
10.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)2.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.917568
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:44.656037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1285591
Coefficient of variation (CV)0.2472687
Kurtosis-0.40511436
Mean36.917568
Median Absolute Deviation (MAD)6
Skewness0.41435485
Sum54638
Variance83.33059
MonotonicityIncreasing
2024-08-26T21:34:44.777533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 78
 
5.3%
36 70
 
4.7%
31 70
 
4.7%
29 69
 
4.7%
32 61
 
4.1%
30 60
 
4.1%
38 58
 
3.9%
33 58
 
3.9%
40 57
 
3.8%
Other values (33) 821
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
0.9%
24 26
1.8%
25 26
1.8%
26 40
2.7%
27 49
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
0.9%
57 4
 
0.3%
56 14
0.9%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

AgeGroup
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size89.7 KiB
26-35
611 
36-45
471 
46-55
228 
18-25
123 
55+
 
47

Length

Max length5
Median length5
Mean length4.9364865
Min length3

Characters and Unicode

Total characters7306
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18-25
2nd row18-25
3rd row18-25
4th row18-25
5th row18-25

Common Values

ValueCountFrequency (%)
26-35 611
41.3%
36-45 471
31.8%
46-55 228
 
15.4%
18-25 123
 
8.3%
55+ 47
 
3.2%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:44.919706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:45.059851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26-35 611
41.3%
36-45 471
31.8%
46-55 228
 
15.4%
18-25 123
 
8.3%
55 47
 
3.2%

Most occurring characters

ValueCountFrequency (%)
5 1755
24.0%
- 1433
19.6%
6 1310
17.9%
3 1082
14.8%
2 734
10.0%
4 699
 
9.6%
1 123
 
1.7%
8 123
 
1.7%
+ 47
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5826
79.7%
Dash Punctuation 1433
 
19.6%
Math Symbol 47
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1755
30.1%
6 1310
22.5%
3 1082
18.6%
2 734
12.6%
4 699
 
12.0%
1 123
 
2.1%
8 123
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 1433
100.0%
Math Symbol
ValueCountFrequency (%)
+ 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1755
24.0%
- 1433
19.6%
6 1310
17.9%
3 1082
14.8%
2 734
10.0%
4 699
 
9.6%
1 123
 
1.7%
8 123
 
1.7%
+ 47
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1755
24.0%
- 1433
19.6%
6 1310
17.9%
3 1082
14.8%
2 734
10.0%
4 699
 
9.6%
1 123
 
1.7%
8 123
 
1.7%
+ 47
 
0.6%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size3.0 KiB
False
1242 
True
238 
(Missing)
 
1
ValueCountFrequency (%)
False 1242
83.9%
True 238
 
16.1%
(Missing) 1
 
0.1%
2024-08-26T21:34:45.177823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

BusinessTravel
Categorical

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size102.0 KiB
Travel_Rarely
1042 
Travel_Frequently
279 
Non-Travel
151 
TravelRarely
 
8

Length

Max length17
Median length13
Mean length13.442568
Min length10

Characters and Unicode

Total characters19895
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Frequently
4th rowNon-Travel
5th rowNon-Travel

Common Values

ValueCountFrequency (%)
Travel_Rarely 1042
70.4%
Travel_Frequently 279
 
18.8%
Non-Travel 151
 
10.2%
TravelRarely 8
 
0.5%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:45.283199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:45.410686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 1042
70.4%
travel_frequently 279
 
18.9%
non-travel 151
 
10.2%
travelrarely 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 3088
15.5%
r 2809
14.1%
l 2809
14.1%
a 2530
12.7%
T 1480
7.4%
v 1480
7.4%
y 1329
6.7%
_ 1321
6.6%
R 1050
 
5.3%
n 430
 
2.2%
Other values (7) 1569
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15463
77.7%
Uppercase Letter 2960
 
14.9%
Connector Punctuation 1321
 
6.6%
Dash Punctuation 151
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3088
20.0%
r 2809
18.2%
l 2809
18.2%
a 2530
16.4%
v 1480
9.6%
y 1329
8.6%
n 430
 
2.8%
q 279
 
1.8%
u 279
 
1.8%
t 279
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 1480
50.0%
R 1050
35.5%
F 279
 
9.4%
N 151
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1321
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18423
92.6%
Common 1472
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3088
16.8%
r 2809
15.2%
l 2809
15.2%
a 2530
13.7%
T 1480
8.0%
v 1480
8.0%
y 1329
7.2%
R 1050
 
5.7%
n 430
 
2.3%
F 279
 
1.5%
Other values (5) 1139
 
6.2%
Common
ValueCountFrequency (%)
_ 1321
89.7%
- 151
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3088
15.5%
r 2809
14.1%
l 2809
14.1%
a 2530
12.7%
T 1480
7.4%
v 1480
7.4%
y 1329
6.7%
_ 1321
6.6%
R 1050
 
5.3%
n 430
 
2.2%
Other values (7) 1569
7.9%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)59.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean801.38446
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:45.528974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile163.95
Q1465
median800
Q31157
95-th percentile1423.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.12699
Coefficient of variation (CV)0.50303819
Kurtosis-1.2019026
Mean801.38446
Median Absolute Deviation (MAD)346
Skewness0.0002461452
Sum1186049
Variance162511.37
MonotonicityNot monotonic
2024-08-26T21:34:45.671054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
1082 5
 
0.3%
1329 5
 
0.3%
530 5
 
0.3%
408 5
 
0.3%
329 5
 
0.3%
350 4
 
0.3%
715 4
 
0.3%
1125 4
 
0.3%
977 4
 
0.3%
Other values (876) 1433
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Department
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size106.5 KiB
Research & Development
967 
Sales
450 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.533108
Min length5

Characters and Unicode

Total characters24469
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowSales
3rd rowSales
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 967
65.3%
Sales 450
30.4%
Human Resources 63
 
4.3%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:45.812257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:45.927847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
research 967
27.8%
967
27.8%
development 967
27.8%
sales 450
12.9%
human 63
 
1.8%
resources 63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 5411
22.1%
1997
 
8.2%
s 1543
 
6.3%
a 1480
 
6.0%
l 1417
 
5.8%
R 1030
 
4.2%
r 1030
 
4.2%
c 1030
 
4.2%
n 1030
 
4.2%
m 1030
 
4.2%
Other values (10) 7471
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18995
77.6%
Uppercase Letter 2510
 
10.3%
Space Separator 1997
 
8.2%
Other Punctuation 967
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5411
28.5%
s 1543
 
8.1%
a 1480
 
7.8%
l 1417
 
7.5%
r 1030
 
5.4%
c 1030
 
5.4%
n 1030
 
5.4%
m 1030
 
5.4%
o 1030
 
5.4%
p 967
 
5.1%
Other values (4) 3027
15.9%
Uppercase Letter
ValueCountFrequency (%)
R 1030
41.0%
D 967
38.5%
S 450
17.9%
H 63
 
2.5%
Space Separator
ValueCountFrequency (%)
1997
100.0%
Other Punctuation
ValueCountFrequency (%)
& 967
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21505
87.9%
Common 2964
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5411
25.2%
s 1543
 
7.2%
a 1480
 
6.9%
l 1417
 
6.6%
R 1030
 
4.8%
r 1030
 
4.8%
c 1030
 
4.8%
n 1030
 
4.8%
m 1030
 
4.8%
o 1030
 
4.8%
Other values (8) 5474
25.5%
Common
ValueCountFrequency (%)
1997
67.4%
& 967
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5411
22.1%
1997
 
8.2%
s 1543
 
6.3%
a 1480
 
6.0%
l 1417
 
5.8%
R 1030
 
4.2%
r 1030
 
4.2%
c 1030
 
4.2%
n 1030
 
4.2%
m 1030
 
4.2%
Other values (10) 7471
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9.2202703
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:46.047373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1312007
Coefficient of variation (CV)0.88188312
Kurtosis-0.23714194
Mean9.2202703
Median Absolute Deviation (MAD)5
Skewness0.95614522
Sum13646
Variance66.116425
MonotonicityNot monotonic
2024-08-26T21:34:46.167799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 212
14.3%
1 208
14.0%
10 86
 
5.8%
9 85
 
5.7%
3 84
 
5.7%
7 84
 
5.7%
8 81
 
5.5%
5 67
 
4.5%
4 65
 
4.4%
6 60
 
4.1%
Other values (19) 448
30.2%
ValueCountFrequency (%)
1 208
14.0%
2 212
14.3%
3 84
 
5.7%
4 65
 
4.4%
5 67
 
4.5%
6 60
 
4.1%
7 84
 
5.7%
8 81
 
5.5%
9 85
5.7%
10 86
5.8%
ValueCountFrequency (%)
29 27
1.8%
28 25
1.7%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 29
2.0%
23 28
1.9%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
3.0
578 
4.0
399 
2.0
283 
1.0
172 
5.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 578
39.0%
4.0 399
26.9%
2.0 283
19.1%
1.0 172
 
11.6%
5.0 48
 
3.2%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:46.278341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:46.378125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 578
39.1%
4.0 399
27.0%
2.0 283
19.1%
1.0 172
 
11.6%
5.0 48
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 578
 
13.0%
4 399
 
9.0%
2 283
 
6.4%
1 172
 
3.9%
5 48
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
3 578
 
19.5%
4 399
 
13.5%
2 283
 
9.6%
1 172
 
5.8%
5 48
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 578
 
13.0%
4 399
 
9.0%
2 283
 
6.4%
1 172
 
3.9%
5 48
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 578
 
13.0%
4 399
 
9.0%
2 283
 
6.4%
1 172
 
3.9%
5 48
 
1.1%

EducationField
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size97.8 KiB
Life Sciences
607 
Medical
470 
Marketing
161 
Technical Degree
132 
Other
83 

Length

Max length16
Median length15
Mean length10.514865
Min length5

Characters and Unicode

Total characters15562
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowMedical
3rd rowMarketing
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 607
41.0%
Medical 470
31.7%
Marketing 161
 
10.9%
Technical Degree 132
 
8.9%
Other 83
 
5.6%
Human Resources 27
 
1.8%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:46.485752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:46.623114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
life 607
27.0%
sciences 607
27.0%
medical 470
20.9%
marketing 161
 
7.2%
technical 132
 
5.9%
degree 132
 
5.9%
other 83
 
3.7%
human 27
 
1.2%
resources 27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 3117
20.0%
i 1977
12.7%
c 1975
12.7%
n 927
 
6.0%
a 790
 
5.1%
766
 
4.9%
s 661
 
4.2%
M 631
 
4.1%
L 607
 
3.9%
f 607
 
3.9%
Other values (16) 3504
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12550
80.6%
Uppercase Letter 2246
 
14.4%
Space Separator 766
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3117
24.8%
i 1977
15.8%
c 1975
15.7%
n 927
 
7.4%
a 790
 
6.3%
s 661
 
5.3%
f 607
 
4.8%
l 602
 
4.8%
d 470
 
3.7%
r 403
 
3.2%
Other values (7) 1021
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M 631
28.1%
L 607
27.0%
S 607
27.0%
T 132
 
5.9%
D 132
 
5.9%
O 83
 
3.7%
H 27
 
1.2%
R 27
 
1.2%
Space Separator
ValueCountFrequency (%)
766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14796
95.1%
Common 766
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3117
21.1%
i 1977
13.4%
c 1975
13.3%
n 927
 
6.3%
a 790
 
5.3%
s 661
 
4.5%
M 631
 
4.3%
L 607
 
4.1%
f 607
 
4.1%
S 607
 
4.1%
Other values (15) 2897
19.6%
Common
ValueCountFrequency (%)
766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3117
20.0%
i 1977
12.7%
c 1975
12.7%
n 927
 
6.0%
a 790
 
5.1%
766
 
4.9%
s 661
 
4.2%
M 631
 
4.1%
L 607
 
3.9%
f 607
 
3.9%
Other values (16) 3504
22.5%

EmployeeCount
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
1.0
1480 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1480
99.9%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:46.765257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:46.860973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1480
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1480
33.3%
. 1480
33.3%
0 1480
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1480
50.0%
0 1480
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1480
33.3%
. 1480
33.3%
0 1480
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1480
33.3%
. 1480
33.3%
0 1480
33.3%

EmployeeNumber
Real number (ℝ)

Distinct1470
Distinct (%)99.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1031.8608
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:46.986911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.95
Q1493.75
median1027.5
Q31568.25
95-th percentile1979.05
Maximum2068
Range2067
Interquartile range (IQR)1074.5

Descriptive statistics

Standard deviation605.95505
Coefficient of variation (CV)0.58724495
Kurtosis-1.22439
Mean1031.8608
Median Absolute Deviation (MAD)536
Skewness0.015233897
Sum1527154
Variance367181.52
MonotonicityNot monotonic
2024-08-26T21:34:47.128080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2062 2
 
0.1%
2055 2
 
0.1%
2057 2
 
0.1%
2065 2
 
0.1%
2064 2
 
0.1%
2061 2
 
0.1%
2068 2
 
0.1%
2054 2
 
0.1%
2056 2
 
0.1%
2060 2
 
0.1%
Other values (1460) 1460
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
2068 2
0.1%
2065 2
0.1%
2064 2
0.1%
2062 2
0.1%
2061 2
0.1%
2060 2
0.1%
2057 2
0.1%
2056 2
0.1%
2055 2
0.1%
2054 2
0.1%
Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
3.0
454 
4.0
451 
2.0
291 
1.0
284 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 454
30.7%
4.0 451
30.5%
2.0 291
19.6%
1.0 284
19.2%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:47.248908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:47.361211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 454
30.7%
4.0 451
30.5%
2.0 291
19.7%
1.0 284
19.2%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 454
 
10.2%
4 451
 
10.2%
2 291
 
6.6%
1 284
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
3 454
 
15.3%
4 451
 
15.2%
2 291
 
9.8%
1 284
 
9.6%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 454
 
10.2%
4 451
 
10.2%
2 291
 
6.6%
1 284
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 454
 
10.2%
4 451
 
10.2%
2 291
 
6.6%
1 284
 
6.4%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size89.5 KiB
Male
889 
Female
591 

Length

Max length6
Median length4
Mean length4.7986486
Min length4

Characters and Unicode

Total characters7102
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 889
60.0%
Female 591
39.9%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:47.644132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:47.743079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 889
60.1%
female 591
39.9%

Most occurring characters

ValueCountFrequency (%)
e 2071
29.2%
a 1480
20.8%
l 1480
20.8%
M 889
12.5%
F 591
 
8.3%
m 591
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5622
79.2%
Uppercase Letter 1480
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2071
36.8%
a 1480
26.3%
l 1480
26.3%
m 591
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M 889
60.1%
F 591
39.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 7102
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2071
29.2%
a 1480
20.8%
l 1480
20.8%
M 889
12.5%
F 591
 
8.3%
m 591
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2071
29.2%
a 1480
20.8%
l 1480
20.8%
M 889
12.5%
F 591
 
8.3%
m 591
 
8.3%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean65.84527
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:47.877226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.328266
Coefficient of variation (CV)0.3087278
Kurtosis-1.1959639
Mean65.84527
Median Absolute Deviation (MAD)18
Skewness-0.03180389
Sum97451
Variance413.23838
MonotonicityNot monotonic
2024-08-26T21:34:48.003335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 29
 
2.0%
66 29
 
2.0%
48 28
 
1.9%
98 28
 
1.9%
84 28
 
1.9%
87 27
 
1.8%
79 27
 
1.8%
57 27
 
1.8%
96 27
 
1.8%
52 26
 
1.8%
Other values (61) 1204
81.3%
ValueCountFrequency (%)
30 20
1.4%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 18
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
3.0
869 
2.0
381 
4.0
147 
1.0
 
83

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 869
58.7%
2.0 381
25.7%
4.0 147
 
9.9%
1.0 83
 
5.6%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:48.146980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:48.260417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 869
58.7%
2.0 381
25.7%
4.0 147
 
9.9%
1.0 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 869
19.6%
2 381
 
8.6%
4 147
 
3.3%
1 83
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
3 869
29.4%
2 381
 
12.9%
4 147
 
5.0%
1 83
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 869
19.6%
2 381
 
8.6%
4 147
 
3.3%
1 83
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 869
19.6%
2 381
 
8.6%
4 147
 
3.3%
1 83
 
1.9%

JobLevel
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
1.0
545 
2.0
539 
3.0
220 
4.0
107 
5.0
69 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 545
36.8%
2.0 539
36.4%
3.0 220
14.9%
4.0 107
 
7.2%
5.0 69
 
4.7%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:48.358611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:48.481320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 545
36.8%
2.0 539
36.4%
3.0 220
14.9%
4.0 107
 
7.2%
5.0 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
1 545
 
12.3%
2 539
 
12.1%
3 220
 
5.0%
4 107
 
2.4%
5 69
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
1 545
 
18.4%
2 539
 
18.2%
3 220
 
7.4%
4 107
 
3.6%
5 69
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
1 545
 
12.3%
2 539
 
12.1%
3 220
 
5.0%
4 107
 
2.4%
5 69
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
1 545
 
12.3%
2 539
 
12.1%
3 220
 
5.0%
4 107
 
2.4%
5 69
 
1.6%

JobRole
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size108.7 KiB
Sales Executive
329 
Research Scientist
293 
Laboratory Technician
261 
Manufacturing Director
147 
Healthcare Representative
132 
Other values (4)
318 

Length

Max length25
Median length21
Mean length18.07973
Min length7

Characters and Unicode

Total characters26758
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowSales Representative
3rd rowSales Representative
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive 329
22.2%
Research Scientist 293
19.8%
Laboratory Technician 261
17.6%
Manufacturing Director 147
9.9%
Healthcare Representative 132
8.9%
Manager 102
 
6.9%
Sales Representative 84
 
5.7%
Research Director 80
 
5.4%
Human Resources 52
 
3.5%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:48.618161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:48.744713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sales 413
14.5%
research 373
13.1%
executive 329
11.5%
scientist 293
10.3%
laboratory 261
9.1%
technician 261
9.1%
director 227
7.9%
representative 216
7.6%
manufacturing 147
 
5.1%
healthcare 132
 
4.6%
Other values (3) 206
7.2%

Most occurring characters

ValueCountFrequency (%)
e 3932
14.7%
a 2599
 
9.7%
t 2114
 
7.9%
c 2075
 
7.8%
i 2027
 
7.6%
r 1998
 
7.5%
n 1479
 
5.5%
s 1399
 
5.2%
1378
 
5.1%
o 801
 
3.0%
Other values (19) 6956
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22522
84.2%
Uppercase Letter 2858
 
10.7%
Space Separator 1378
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3932
17.5%
a 2599
11.5%
t 2114
9.4%
c 2075
9.2%
i 2027
9.0%
r 1998
8.9%
n 1479
 
6.6%
s 1399
 
6.2%
o 801
 
3.6%
h 766
 
3.4%
Other values (10) 3332
14.8%
Uppercase Letter
ValueCountFrequency (%)
S 706
24.7%
R 641
22.4%
E 329
11.5%
L 261
 
9.1%
T 261
 
9.1%
M 249
 
8.7%
D 227
 
7.9%
H 184
 
6.4%
Space Separator
ValueCountFrequency (%)
1378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25380
94.9%
Common 1378
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3932
15.5%
a 2599
10.2%
t 2114
 
8.3%
c 2075
 
8.2%
i 2027
 
8.0%
r 1998
 
7.9%
n 1479
 
5.8%
s 1399
 
5.5%
o 801
 
3.2%
h 766
 
3.0%
Other values (18) 6190
24.4%
Common
ValueCountFrequency (%)
1378
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3932
14.7%
a 2599
 
9.7%
t 2114
 
7.9%
c 2075
 
7.8%
i 2027
 
7.6%
r 1998
 
7.5%
n 1479
 
5.5%
s 1399
 
5.2%
1378
 
5.1%
o 801
 
3.0%
Other values (19) 6956
26.0%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
4.0
461 
3.0
444 
1.0
293 
2.0
282 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 461
31.1%
3.0 444
30.0%
1.0 293
19.8%
2.0 282
19.0%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:48.897054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:49.019408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 461
31.1%
3.0 444
30.0%
1.0 293
19.8%
2.0 282
19.1%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
4 461
 
10.4%
3 444
 
10.0%
1 293
 
6.6%
2 282
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
4 461
 
15.6%
3 444
 
15.0%
1 293
 
9.9%
2 282
 
9.5%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
4 461
 
10.4%
3 444
 
10.0%
1 293
 
6.6%
2 282
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
4 461
 
10.4%
3 444
 
10.0%
1 293
 
6.6%
2 282
 
6.4%

MaritalStatus
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size92.5 KiB
Married
679 
Single
473 
Divorced
328 

Length

Max length8
Median length7
Mean length6.902027
Min length6

Characters and Unicode

Total characters10215
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 679
45.8%
Single 473
31.9%
Divorced 328
22.1%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:49.149119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:49.263052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
married 679
45.9%
single 473
32.0%
divorced 328
22.2%

Most occurring characters

ValueCountFrequency (%)
r 1686
16.5%
i 1480
14.5%
e 1480
14.5%
d 1007
9.9%
M 679
6.6%
a 679
6.6%
S 473
 
4.6%
n 473
 
4.6%
g 473
 
4.6%
l 473
 
4.6%
Other values (4) 1312
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8735
85.5%
Uppercase Letter 1480
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1686
19.3%
i 1480
16.9%
e 1480
16.9%
d 1007
11.5%
a 679
7.8%
n 473
 
5.4%
g 473
 
5.4%
l 473
 
5.4%
v 328
 
3.8%
o 328
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 679
45.9%
S 473
32.0%
D 328
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 10215
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1686
16.5%
i 1480
14.5%
e 1480
14.5%
d 1007
9.9%
M 679
6.6%
a 679
6.6%
S 473
 
4.6%
n 473
 
4.6%
g 473
 
4.6%
l 473
 
4.6%
Other values (4) 1312
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1686
16.5%
i 1480
14.5%
e 1480
14.5%
d 1007
9.9%
M 679
6.6%
a 679
6.6%
S 473
 
4.6%
n 473
 
4.6%
g 473
 
4.6%
l 473
 
4.6%
Other values (4) 1312
12.8%

MonthlyIncome
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)91.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6504.9858
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:49.379627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2098.9
Q12922.25
median4933
Q38383.75
95-th percentile17782.85
Maximum19999
Range18990
Interquartile range (IQR)5461.5

Descriptive statistics

Standard deviation4700.2614
Coefficient of variation (CV)0.7225629
Kurtosis1.0046516
Mean6504.9858
Median Absolute Deviation (MAD)2209
Skewness1.3672165
Sum9627379
Variance22092457
MonotonicityNot monotonic
2024-08-26T21:34:49.514844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6142 4
 
0.3%
2342 4
 
0.3%
2610 3
 
0.2%
6347 3
 
0.2%
2380 3
 
0.2%
2404 3
 
0.2%
5562 3
 
0.2%
2451 3
 
0.2%
2741 3
 
0.2%
2559 3
 
0.2%
Other values (1339) 1448
97.8%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

SalarySlab
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size91.9 KiB
Upto 5k
753 
5k-10k
444 
10k-15k
150 
15k+
133 

Length

Max length7
Median length7
Mean length6.4304054
Min length4

Characters and Unicode

Total characters9517
Distinct characters11
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUpto 5k
2nd rowUpto 5k
3rd rowUpto 5k
4th rowUpto 5k
5th rowUpto 5k

Common Values

ValueCountFrequency (%)
Upto 5k 753
50.8%
5k-10k 444
30.0%
10k-15k 150
 
10.1%
15k+ 133
 
9.0%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:49.642879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:49.787553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
upto 753
33.7%
5k 753
33.7%
5k-10k 444
19.9%
10k-15k 150
 
6.7%
15k 133
 
6.0%

Most occurring characters

ValueCountFrequency (%)
k 2074
21.8%
5 1480
15.6%
1 877
9.2%
U 753
 
7.9%
p 753
 
7.9%
t 753
 
7.9%
o 753
 
7.9%
753
 
7.9%
- 594
 
6.2%
0 594
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4333
45.5%
Decimal Number 2951
31.0%
Uppercase Letter 753
 
7.9%
Space Separator 753
 
7.9%
Dash Punctuation 594
 
6.2%
Math Symbol 133
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k 2074
47.9%
p 753
 
17.4%
t 753
 
17.4%
o 753
 
17.4%
Decimal Number
ValueCountFrequency (%)
5 1480
50.2%
1 877
29.7%
0 594
20.1%
Uppercase Letter
ValueCountFrequency (%)
U 753
100.0%
Space Separator
ValueCountFrequency (%)
753
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 594
100.0%
Math Symbol
ValueCountFrequency (%)
+ 133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5086
53.4%
Common 4431
46.6%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1480
33.4%
1 877
19.8%
753
17.0%
- 594
13.4%
0 594
13.4%
+ 133
 
3.0%
Latin
ValueCountFrequency (%)
k 2074
40.8%
U 753
 
14.8%
p 753
 
14.8%
t 753
 
14.8%
o 753
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 2074
21.8%
5 1480
15.6%
1 877
9.2%
U 753
 
7.9%
p 753
 
7.9%
t 753
 
7.9%
o 753
 
7.9%
753
 
7.9%
- 594
 
6.2%
0 594
 
6.2%

MonthlyRate
Real number (ℝ)

Distinct1427
Distinct (%)96.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14298.461
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:49.924257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3394.05
Q18051
median14220
Q320460.5
95-th percentile25422.9
Maximum26999
Range24905
Interquartile range (IQR)12409.5

Descriptive statistics

Standard deviation7112.0568
Coefficient of variation (CV)0.49740017
Kurtosis-1.2140902
Mean14298.461
Median Absolute Deviation (MAD)6206.5
Skewness0.021593261
Sum21161722
Variance50581352
MonotonicityNot monotonic
2024-08-26T21:34:50.093347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9150 3
 
0.2%
4223 3
 
0.2%
6670 2
 
0.1%
2755 2
 
0.1%
11162 2
 
0.1%
9558 2
 
0.1%
23016 2
 
0.1%
15986 2
 
0.1%
3787 2
 
0.1%
22074 2
 
0.1%
Other values (1417) 1458
98.4%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

NumCompaniesWorked
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.6871622
Minimum0
Maximum9
Zeros200
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:50.220855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.494098
Coefficient of variation (CV)0.92815313
Kurtosis0.021783118
Mean2.6871622
Median Absolute Deviation (MAD)1
Skewness1.0288762
Sum3977
Variance6.2205247
MonotonicityNot monotonic
2024-08-26T21:34:50.326339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 523
35.3%
0 200
 
13.5%
3 159
 
10.7%
2 148
 
10.0%
4 142
 
9.6%
7 74
 
5.0%
6 70
 
4.7%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
(Missing) 1
 
0.1%
ValueCountFrequency (%)
0 200
 
13.5%
1 523
35.3%
2 148
 
10.0%
3 159
 
10.7%
4 142
 
9.6%
5 63
 
4.3%
6 70
 
4.7%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.7%
5 63
 
4.3%
4 142
 
9.6%
3 159
 
10.7%
2 148
 
10.0%
1 523
35.3%
0 200
 
13.5%

Over18
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size3.0 KiB
True
1480 
(Missing)
 
1
ValueCountFrequency (%)
True 1480
99.9%
(Missing) 1
 
0.1%
2024-08-26T21:34:50.424903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size3.0 KiB
False
1062 
True
418 
(Missing)
 
1
ValueCountFrequency (%)
False 1062
71.7%
True 418
 
28.2%
(Missing) 1
 
0.1%
2024-08-26T21:34:50.536970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15.210135
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:50.644146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6553376
Coefficient of variation (CV)0.24032249
Kurtosis-0.30115938
Mean15.210135
Median Absolute Deviation (MAD)2
Skewness0.81911775
Sum22511
Variance13.361493
MonotonicityNot monotonic
2024-08-26T21:34:50.755213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 211
14.2%
13 210
14.2%
14 203
13.7%
12 199
13.4%
15 102
6.9%
18 90
6.1%
17 83
 
5.6%
16 78
 
5.3%
19 77
 
5.2%
22 56
 
3.8%
Other values (5) 171
11.5%
ValueCountFrequency (%)
11 211
14.2%
12 199
13.4%
13 210
14.2%
14 203
13.7%
15 102
6.9%
16 78
 
5.3%
17 83
 
5.6%
18 90
6.1%
19 77
 
5.2%
20 56
 
3.8%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.2%
20 56
3.8%
19 77
5.2%
18 90
6.1%
17 83
5.6%
16 78
5.3%

PerformanceRating
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
3.0
1253 
4.0
227 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 1253
84.6%
4.0 227
 
15.3%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:50.876405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:50.988189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1253
84.7%
4.0 227
 
15.3%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 1253
28.2%
4 227
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
3 1253
42.3%
4 227
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 1253
28.2%
4 227
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 1253
28.2%
4 227
 
5.1%
Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
3.0
460 
4.0
434 
2.0
307 
1.0
279 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 460
31.1%
4.0 434
29.3%
2.0 307
20.7%
1.0 279
18.8%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:51.108552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:51.220216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 460
31.1%
4.0 434
29.3%
2.0 307
20.7%
1.0 279
18.9%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 460
 
10.4%
4 434
 
9.8%
2 307
 
6.9%
1 279
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
3 460
 
15.5%
4 434
 
14.7%
2 307
 
10.4%
1 279
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 460
 
10.4%
4 434
 
9.8%
2 307
 
6.9%
1 279
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 460
 
10.4%
4 434
 
9.8%
2 307
 
6.9%
1 279
 
6.3%

StandardHours
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size88.4 KiB
80.0
1480 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5920
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80.0
2nd row80.0
3rd row80.0
4th row80.0
5th row80.0

Common Values

ValueCountFrequency (%)
80.0 1480
99.9%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:51.339433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:51.428214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
80.0 1480
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2960
50.0%
8 1480
25.0%
. 1480
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4440
75.0%
Other Punctuation 1480
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2960
66.7%
8 1480
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2960
50.0%
8 1480
25.0%
. 1480
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2960
50.0%
8 1480
25.0%
. 1480
25.0%

StockOptionLevel
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
0.0
636 
1.0
601 
2.0
158 
3.0
85 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 636
42.9%
1.0 601
40.6%
2.0 158
 
10.7%
3.0 85
 
5.7%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:51.515049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:51.620524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 636
43.0%
1.0 601
40.6%
2.0 158
 
10.7%
3.0 85
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 2116
47.7%
. 1480
33.3%
1 601
 
13.5%
2 158
 
3.6%
3 85
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2116
71.5%
1 601
 
20.3%
2 158
 
5.3%
3 85
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2116
47.7%
. 1480
33.3%
1 601
 
13.5%
2 158
 
3.6%
3 85
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2116
47.7%
. 1480
33.3%
1 601
 
13.5%
2 158
 
3.6%
3 85
 
1.9%

TotalWorkingYears
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)2.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.281757
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:51.726678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7708696
Coefficient of variation (CV)0.68879961
Kurtosis0.91435956
Mean11.281757
Median Absolute Deviation (MAD)4
Skewness1.1146854
Sum16697
Variance60.386415
MonotonicityNot monotonic
2024-08-26T21:34:51.861505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 203
 
13.7%
6 127
 
8.6%
8 103
 
7.0%
9 97
 
6.5%
5 90
 
6.1%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.2%
3 42
 
2.8%
Other values (30) 545
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.8%
4 63
4.3%
5 90
6.1%
6 127
8.6%
7 81
5.5%
8 103
7.0%
9 97
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.797973
Minimum0
Maximum6
Zeros55
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:51.963628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2887913
Coefficient of variation (CV)0.46061607
Kurtosis0.49992211
Mean2.797973
Median Absolute Deviation (MAD)1
Skewness0.54909278
Sum4141
Variance1.660983
MonotonicityNot monotonic
2024-08-26T21:34:52.042665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 550
37.1%
3 496
33.5%
4 123
 
8.3%
5 120
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 55
 
3.7%
(Missing) 1
 
0.1%
ValueCountFrequency (%)
0 55
 
3.7%
1 71
 
4.8%
2 550
37.1%
3 496
33.5%
4 123
 
8.3%
5 120
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 120
 
8.1%
4 123
 
8.3%
3 496
33.5%
2 550
37.1%
1 71
 
4.8%
0 55
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.9 KiB
3.0
899 
2.0
346 
4.0
154 
1.0
 
81

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4440
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 899
60.7%
2.0 346
 
23.4%
4.0 154
 
10.4%
1.0 81
 
5.5%
(Missing) 1
 
0.1%

Length

2024-08-26T21:34:52.159933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T21:34:52.252738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 899
60.7%
2.0 346
 
23.4%
4.0 154
 
10.4%
1.0 81
 
5.5%

Most occurring characters

ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 899
20.2%
2 346
 
7.8%
4 154
 
3.5%
1 81
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2960
66.7%
Other Punctuation 1480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1480
50.0%
3 899
30.4%
2 346
 
11.7%
4 154
 
5.2%
1 81
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 1480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 899
20.2%
2 346
 
7.8%
4 154
 
3.5%
1 81
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1480
33.3%
0 1480
33.3%
3 899
20.2%
2 346
 
7.8%
4 154
 
3.5%
1 81
 
1.8%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)2.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.0094595
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:52.520509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1179453
Coefficient of variation (CV)0.87281271
Kurtosis3.9397234
Mean7.0094595
Median Absolute Deviation (MAD)3
Skewness1.7654582
Sum10374
Variance37.429255
MonotonicityNot monotonic
2024-08-26T21:34:52.660085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 198
13.4%
1 171
11.5%
3 129
8.7%
2 127
8.6%
10 120
8.1%
4 112
 
7.6%
7 91
 
6.1%
9 84
 
5.7%
8 80
 
5.4%
6 77
 
5.2%
Other values (27) 291
19.6%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.5%
2 127
8.6%
3 129
8.7%
4 112
7.6%
5 198
13.4%
6 77
 
5.2%
7 91
6.1%
8 80
5.4%
9 84
5.7%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.2283784
Minimum0
Maximum18
Zeros244
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:52.781762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6160195
Coefficient of variation (CV)0.85517879
Kurtosis0.48475557
Mean4.2283784
Median Absolute Deviation (MAD)3
Skewness0.9185461
Sum6258
Variance13.075597
MonotonicityNot monotonic
2024-08-26T21:34:52.912610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 376
25.4%
0 244
16.5%
7 223
15.1%
3 136
 
9.2%
4 106
 
7.2%
8 89
 
6.0%
9 68
 
4.6%
1 57
 
3.8%
6 38
 
2.6%
5 36
 
2.4%
Other values (9) 107
 
7.2%
ValueCountFrequency (%)
0 244
16.5%
1 57
 
3.8%
2 376
25.4%
3 136
 
9.2%
4 106
 
7.2%
5 36
 
2.4%
6 38
 
2.6%
7 223
15.1%
8 89
 
6.0%
9 68
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
0.9%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 68
4.6%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.1824324
Minimum0
Maximum15
Zeros586
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:53.030867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2193574
Coefficient of variation (CV)1.4751235
Kurtosis3.6204468
Mean2.1824324
Median Absolute Deviation (MAD)1
Skewness1.9867214
Sum3230
Variance10.364262
MonotonicityNot monotonic
2024-08-26T21:34:53.156744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 586
39.6%
1 360
24.3%
2 160
 
10.8%
7 76
 
5.1%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.0%
6 32
 
2.2%
11 24
 
1.6%
9 18
 
1.2%
Other values (6) 66
 
4.5%
ValueCountFrequency (%)
0 586
39.6%
1 360
24.3%
2 160
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.0%
6 32
 
2.2%
7 76
 
5.1%
8 18
 
1.2%
9 18
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 18
 
1.2%
8 18
 
1.2%
7 76
5.1%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)1.3%
Missing58
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean4.1180604
Minimum0
Maximum17
Zeros253
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-08-26T21:34:53.270209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5554841
Coefficient of variation (CV)0.86338802
Kurtosis0.14961566
Mean4.1180604
Median Absolute Deviation (MAD)3
Skewness0.82788167
Sum5860
Variance12.641467
MonotonicityNot monotonic
2024-08-26T21:34:53.399194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 330
22.3%
0 253
17.1%
7 211
14.2%
3 139
9.4%
8 100
 
6.8%
4 95
 
6.4%
1 76
 
5.1%
9 61
 
4.1%
5 31
 
2.1%
6 30
 
2.0%
Other values (8) 97
 
6.5%
(Missing) 58
 
3.9%
ValueCountFrequency (%)
0 253
17.1%
1 76
 
5.1%
2 330
22.3%
3 139
9.4%
4 95
 
6.4%
5 31
 
2.1%
6 30
 
2.0%
7 211
14.2%
8 100
 
6.8%
9 61
 
4.1%
ValueCountFrequency (%)
17 6
 
0.4%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
0.9%
12 17
 
1.1%
11 21
 
1.4%
10 27
 
1.8%
9 61
4.1%
8 100
6.8%

Interactions

2024-08-26T21:34:40.266235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:17.133322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:19.031265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:20.573210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:22.181212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:23.615775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:25.117352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:26.759919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:28.296838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:29.731633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:31.235726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:33.102071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:35.111724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:36.831582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:38.677702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:40.350945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:17.421396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:19.142750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:20.665285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:22.276163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:23.702781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:25.201532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:26.838312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:28.392289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:29.829219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-08-26T21:34:36.693727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:38.418721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-26T21:34:40.146106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-26T21:34:53.539857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAgeGroupAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionSalarySlabStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.9100.2110.0370.0080.000-0.0180.1510.000-0.0030.0140.0000.0270.0280.2950.1750.0000.1410.4720.0190.3550.0000.0040.0000.0380.3250.0930.6580.0040.0350.2510.1980.1750.201
AgeGroup0.9101.0000.1930.0370.0260.0000.0000.1480.0160.0000.0140.0000.0480.0000.2750.2330.0000.0890.2680.0760.2200.0000.0080.0000.0230.3100.0660.4270.0000.0190.2440.1440.1200.133
Attrition0.2110.1931.0000.1230.0620.0790.0660.0000.0900.0000.1150.0110.0450.1320.2160.2290.1000.1710.2150.0150.1070.2450.0000.0000.0360.1610.1950.2070.0770.0930.1750.1690.0280.187
BusinessTravel0.0370.0370.1231.0000.0230.0000.0110.0000.0000.0780.0180.0360.0000.0110.0000.0000.0000.0360.0000.0000.0000.0240.0550.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.045
DailyRate0.0080.0260.0620.0231.0000.000-0.0050.0220.039-0.0560.0000.0320.0220.0230.0000.0000.0000.0830.013-0.0290.0370.0000.0240.0000.0000.0000.0410.021-0.0100.018-0.0100.007-0.038-0.002
Department0.0000.0000.0790.0000.0001.0000.0000.0000.5880.0370.0140.0270.0000.0000.2100.9370.0290.0290.1860.0000.0330.0000.0000.0000.0220.1740.0000.0170.0000.0470.0000.0000.0000.000
DistanceFromHome-0.0180.0000.0660.011-0.0050.0001.0000.0000.0000.0430.0000.0350.0180.0370.0530.0000.0000.0000.0030.038-0.0090.0610.0270.0520.0350.0370.011-0.001-0.0230.0000.0100.014-0.003-0.001
Education0.1510.1480.0000.0000.0220.0000.0001.0000.0530.0430.0140.0000.0000.0000.0850.0500.0190.0000.0920.0390.0990.0000.0210.0000.0190.0720.0260.0940.0260.0000.0700.0260.0000.000
EducationField0.0000.0160.0900.0000.0390.5880.0000.0531.0000.0000.0340.0000.0320.0000.0910.3360.0170.0000.0740.0000.0620.0000.0000.0000.0380.0970.0330.0290.0450.0260.0000.0000.0000.000
EmployeeNumber-0.0030.0000.0000.078-0.0560.0370.0430.0430.0001.0000.0000.0520.0300.0400.0370.0000.0000.0000.0050.0080.0030.018-0.0070.0270.0570.0410.069-0.0030.0260.0000.0150.0000.004-0.005
EnvironmentSatisfaction0.0140.0140.1150.0180.0000.0140.0000.0140.0340.0001.0000.0000.0090.0370.0000.0000.0000.0240.0000.0000.0000.0580.0000.0000.0000.0170.0000.0000.0000.0000.0330.0360.0000.000
Gender0.0000.0000.0110.0360.0320.0270.0350.0000.0000.0520.0001.0000.0000.0000.0460.0740.0000.0350.0470.0000.0000.0280.0440.0000.0000.0000.0000.0000.0000.0000.0690.0760.0000.000
HourlyRate0.0270.0480.0450.0000.0220.0000.0180.0000.0320.0300.0090.0001.0000.0000.0000.0240.0180.000-0.019-0.0190.0180.060-0.0100.0000.0000.0000.053-0.014-0.0020.000-0.028-0.033-0.0520.003
JobInvolvement0.0280.0000.1320.0110.0230.0000.0370.0000.0000.0400.0370.0000.0001.0000.0000.0000.0000.0290.0490.0060.0000.0000.0400.0000.0000.0300.0200.0000.0250.0000.0540.0000.0000.053
JobLevel0.2950.2750.2160.0000.0000.2100.0530.0850.0910.0370.0000.0460.0000.0001.0000.5680.0000.0500.8620.0200.1130.0000.0000.0000.0000.7490.0700.5400.0160.0000.3530.2410.2050.231
JobRole0.1750.2330.2290.0000.0000.9370.0000.0500.3360.0000.0000.0740.0240.0000.5681.0000.0000.0630.4220.0000.0790.0000.0000.0000.0340.6570.0400.2920.0000.0300.1880.1320.1110.117
JobSatisfaction0.0000.0000.1000.0000.0000.0290.0000.0190.0170.0000.0000.0000.0180.0000.0000.0001.0000.0000.0000.0440.0000.0170.0000.0250.0000.0000.0000.0230.0220.0000.0000.0000.0000.000
MaritalStatus0.1410.0890.1710.0360.0830.0290.0000.0000.0000.0000.0240.0350.0000.0290.0500.0630.0001.0000.0630.0000.0430.0000.0000.0000.0290.0640.5810.0690.0000.0000.0000.0440.0360.000
MonthlyIncome0.4720.2680.2150.0000.0130.1860.0030.0920.0740.0050.0000.047-0.0190.0490.8620.4220.0000.0631.0000.0530.1880.000-0.0350.0000.0430.9330.0590.709-0.0350.0000.4640.3950.2660.365
MonthlyRate0.0190.0760.0150.000-0.0290.0000.0380.0390.0000.0080.0000.000-0.0190.0060.0200.0000.0440.0000.0531.0000.0220.000-0.0060.0230.0580.0000.0000.013-0.0070.034-0.032-0.007-0.016-0.034
NumCompaniesWorked0.3550.2200.1070.0000.0370.033-0.0090.0990.0620.0030.0000.0000.0180.0000.1130.0790.0000.0430.1880.0221.0000.000-0.0010.0030.0000.1350.0000.315-0.0420.048-0.173-0.128-0.066-0.139
OverTime0.0000.0000.2450.0240.0000.0000.0610.0000.0000.0180.0580.0280.0600.0000.0000.0000.0170.0000.0000.0000.0001.0000.0000.0000.0280.0360.0000.0000.1000.0000.0230.0440.0160.000
PercentSalaryHike0.0040.0080.0000.0550.0240.0000.0270.0210.000-0.0070.0000.044-0.0100.0400.0000.0000.0000.000-0.035-0.006-0.0010.0001.0000.9970.0270.0000.000-0.027-0.0060.000-0.054-0.027-0.059-0.030
PerformanceRating0.0000.0000.0000.0000.0000.0000.0520.0000.0000.0270.0000.0000.0000.0000.0000.0000.0250.0000.0000.0230.0030.0000.9971.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.000
RelationshipSatisfaction0.0380.0230.0360.0120.0000.0220.0350.0190.0380.0570.0000.0000.0000.0000.0000.0340.0000.0290.0430.0580.0000.0280.0270.0001.0000.0000.0310.0300.0000.0000.0000.0000.0520.000
SalarySlab0.3250.3100.1610.0000.0000.1740.0370.0720.0970.0410.0170.0000.0000.0300.7490.6570.0000.0640.9330.0000.1350.0360.0000.0000.0001.0000.0630.5820.0160.0000.3650.2590.2400.237
StockOptionLevel0.0930.0660.1950.0000.0410.0000.0110.0260.0330.0690.0000.0000.0530.0200.0700.0400.0000.5810.0590.0000.0000.0000.0000.0000.0310.0631.0000.0650.0000.0190.0120.0260.0570.033
TotalWorkingYears0.6580.4270.2070.0000.0210.017-0.0010.0940.029-0.0030.0000.000-0.0140.0000.5400.2920.0230.0690.7090.0130.3150.000-0.0270.0000.0300.5820.0651.000-0.0130.0000.5930.4920.3350.493
TrainingTimesLastYear0.0040.0000.0770.000-0.0100.000-0.0230.0260.0450.0260.0000.000-0.0020.0250.0160.0000.0220.000-0.035-0.007-0.0420.100-0.0060.0000.0000.0160.000-0.0131.0000.0000.0000.0050.010-0.012
WorkLifeBalance0.0350.0190.0930.0000.0180.0470.0000.0000.0260.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0340.0480.0000.0000.0000.0000.0000.0190.0000.0001.0000.0150.0270.0000.045
YearsAtCompany0.2510.2440.1750.000-0.0100.0000.0100.0700.0000.0150.0330.069-0.0280.0540.3530.1880.0000.0000.464-0.032-0.1730.023-0.0540.0000.0000.3650.0120.5930.0000.0151.0000.8540.5180.837
YearsInCurrentRole0.1980.1440.1690.0000.0070.0000.0140.0260.0000.0000.0360.076-0.0330.0000.2410.1320.0000.0440.395-0.007-0.1280.044-0.0270.0270.0000.2590.0260.4920.0050.0270.8541.0000.5050.721
YearsSinceLastPromotion0.1750.1200.0280.000-0.0380.000-0.0030.0000.0000.0040.0000.000-0.0520.0000.2050.1110.0000.0360.266-0.016-0.0660.016-0.0590.0000.0520.2400.0570.3350.0100.0000.5180.5051.0000.462
YearsWithCurrManager0.2010.1330.1870.045-0.0020.000-0.0010.0000.000-0.0050.0000.0000.0030.0530.2310.1170.0000.0000.365-0.034-0.1390.000-0.0300.0000.0000.2370.0330.493-0.0120.0450.8370.7210.4621.000

Missing values

2024-08-26T21:34:41.934457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-26T21:34:42.420088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-26T21:34:43.150092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EmpIDAgeAgeGroupAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeSalarySlabMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
0RM29718.018-25YesTravel_Rarely230.0Research & Development3.03.0Life Sciences1.0405.03.0Male54.03.01.0Laboratory Technician3.0Single1420.0Upto 5k25233.01.0YNo13.03.03.080.00.00.02.03.00.00.00.00.0
1RM30218.018-25NoTravel_Rarely812.0Sales10.03.0Medical1.0411.04.0Female69.02.01.0Sales Representative3.0Single1200.0Upto 5k9724.01.0YNo12.03.01.080.00.00.02.03.00.00.00.00.0
2RM45818.018-25YesTravel_Frequently1306.0Sales5.03.0Marketing1.0614.02.0Male69.03.01.0Sales Representative2.0Single1878.0Upto 5k8059.01.0YYes14.03.04.080.00.00.03.03.00.00.00.00.0
3RM72818.018-25NoNon-Travel287.0Research & Development5.02.0Life Sciences1.01012.02.0Male73.03.01.0Research Scientist4.0Single1051.0Upto 5k13493.01.0YNo15.03.04.080.00.00.02.03.00.00.00.00.0
4RM82918.018-25YesNon-Travel247.0Research & Development8.01.0Medical1.01156.03.0Male80.03.01.0Laboratory Technician3.0Single1904.0Upto 5k13556.01.0YNo12.03.04.080.00.00.00.03.00.00.00.00.0
5RM97318.018-25NoNon-Travel1124.0Research & Development1.03.0Life Sciences1.01368.04.0Female97.03.01.0Laboratory Technician4.0Single1611.0Upto 5k19305.01.0YNo15.03.03.080.00.00.05.04.00.00.00.00.0
6RM115418.018-25YesTravel_Frequently544.0Sales3.02.0Medical1.01624.02.0Female70.03.01.0Sales Representative4.0Single1569.0Upto 5k18420.01.0YYes12.03.03.080.00.00.02.04.00.00.00.00.0
7RM131218.018-25NoNon-Travel1431.0Research & Development14.03.0Medical1.01839.02.0Female33.03.01.0Research Scientist3.0Single1514.0Upto 5k8018.01.0YNo16.03.03.080.00.00.04.01.00.00.00.00.0
8RM12819.018-25YesTravel_Rarely528.0Sales22.01.0Marketing1.0167.04.0Male50.03.01.0Sales Representative3.0Single1675.0Upto 5k26820.01.0YYes19.03.04.080.00.00.02.02.00.00.00.00.0
9RM15019.018-25NoTravel_Rarely1181.0Research & Development3.01.0Medical1.0201.02.0Female79.03.01.0Laboratory Technician2.0Single1483.0Upto 5k16102.01.0YNo14.03.04.080.00.01.03.03.01.00.00.00.0
EmpIDAgeAgeGroupAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeSalarySlabMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
1471RM74459.055+NoTravel_Rarely715.0Research & Development2.03.0Life Sciences1.01032.03.0Female69.02.04.0Manufacturing Director4.0Single13726.010k-15k21829.03.0YYes13.03.01.080.00.030.04.03.05.03.04.03.0
1472RM75959.055+NoTravel_Rarely1089.0Sales1.02.0Technical Degree1.01048.02.0Male66.03.03.0Manager4.0Married11904.010k-15k11038.03.0YYes14.03.03.080.01.014.01.01.06.04.00.04.0
1473RM89859.055+NoTravel_Rarely326.0Sales3.03.0Life Sciences1.01254.03.0Female48.02.02.0Sales Executive4.0Single5171.05k-10k16490.05.0YNo17.03.04.080.00.013.02.03.06.01.00.05.0
1474RM92059.055+NoTravel_Rarely1429.0Research & Development18.04.0Medical1.01283.04.0Male67.03.03.0Manufacturing Director4.0Single10512.010k-15k20002.06.0YNo12.03.04.080.00.025.06.02.09.07.05.04.0
1475RM41260.055+NoTravel_Rarely422.0Research & Development7.03.0Life Sciences1.0549.01.0Female41.03.05.0Manager1.0Married19566.015k+3854.05.0YNo11.03.04.080.00.033.05.01.029.08.011.010.0
1476RM42860.055+NoTravel_Frequently1499.0Sales28.03.0Marketing1.0573.03.0Female80.02.03.0Sales Executive1.0Married10266.010k-15k2845.04.0YNo19.03.04.080.00.022.05.04.018.013.013.011.0
1477RM53760.055+NoTravel_Rarely1179.0Sales16.04.0Marketing1.0732.01.0Male84.03.02.0Sales Executive1.0Single5405.05k-10k11924.08.0YNo14.03.04.080.00.010.01.03.02.02.02.02.0
1478RM88060.055+NoTravel_Rarely696.0Sales7.04.0Marketing1.01233.02.0Male52.04.02.0Sales Executive4.0Divorced5220.05k-10k10893.00.0YYes18.03.02.080.01.012.03.03.011.07.01.09.0
1479RM121060.055+NoTravel_Rarely370.0Research & Development1.04.0Medical1.01697.03.0Male92.01.03.0Healthcare Representative4.0Divorced10883.010k-15k20467.03.0YNo20.04.03.080.01.019.02.04.01.00.00.00.0
1480import pandas as pandasNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

EmpIDAgeAgeGroupAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeSalarySlabMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager# duplicates
0RM146129.026-35NoTravel_Rarely468.0Research & Development28.04.0Medical1.02054.04.0Female73.02.01.0Research Scientist1.0Single3785.0Upto 5k8489.01.0YNo14.03.02.080.00.05.03.01.05.04.00.04.02
1RM146250.046-55YesTravel_Rarely410.0Sales28.03.0Marketing1.02055.04.0Male39.02.03.0Sales Executive1.0Divorced10854.010k-15k16586.04.0YYes13.03.02.080.01.020.03.03.03.02.02.00.02
2RM146339.036-45NoTravel_Rarely722.0Sales24.01.0Marketing1.02056.02.0Female60.02.04.0Sales Executive4.0Married12031.010k-15k8828.00.0YNo11.03.01.080.01.021.02.02.020.09.09.06.02
3RM146431.026-35NoNon-Travel325.0Research & Development5.03.0Medical1.02057.02.0Male74.03.02.0Manufacturing Director1.0Single9936.05k-10k3787.00.0YNo19.03.02.080.00.010.02.03.09.04.01.07.02
4RM146827.026-35NoTravel_Rarely155.0Research & Development4.03.0Life Sciences1.02064.02.0Male87.04.02.0Manufacturing Director2.0Married6142.05k-10k5174.01.0YYes20.04.02.080.01.06.00.03.06.02.00.03.02
5RM146949.046-55NoTravel_Frequently1023.0Sales2.03.0Medical1.02065.04.0Male63.02.02.0Sales Executive2.0Married5390.05k-10k13243.02.0YNo14.03.04.080.00.017.03.02.09.06.00.08.02
6RM147034.026-35NoTravelRarely628.0Research & Development8.03.0Medical1.02068.02.0Male82.04.02.0Laboratory Technician3.0Married4404.0Upto 5k10228.02.0YNo12.03.01.080.00.06.03.04.04.03.01.02.02